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signals</title> <meta name="description" content="Search results for: signals"> <meta name="keywords" content="signals"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="signals"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 992</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: signals</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">902</span> Application of Envelope Spectrum Analysis and Spectral Kurtosis to Diagnose Debris Fault in Bearing Using Acoustic Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Henry%20Ogbemudia%20Omoregbee">Henry Ogbemudia Omoregbee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mabel%20Usunobun%20Olanipekun"> Mabel Usunobun Olanipekun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Debris fault diagnosis based on acoustic signals in rolling element bearing running at low speed and high radial loads are more of low amplitudes, particularly in the case of debris faults whose signals necessitate high sensitivity analyses. As the rollers in the bearing roll over debris trapped in grease used to lubricate the bearings, the envelope signal created by amplitude demodulation carries additional diagnostic information that is not available through ordinary spectrum analysis of the raw signal. The kurtosis value obtained for three different scenarios (debris induced, outer crack induced, and a normal good bearing) couldn't be used to easily identify whether the used bearings were defective or not. It was established in this work that the envelope spectrum analysis detected the fault signature and its harmonics induced in the debris bearings when bandpass filtering of the raw signal with the frequency band specified by kurtogram and spectral kurtosis was made. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rolling%20bearings" title="rolling bearings">rolling bearings</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20element%20bearing%20noise" title=" rolling element bearing noise"> rolling element bearing noise</a>, <a href="https://publications.waset.org/abstracts/search?q=bandpass%20filtering" title=" bandpass filtering"> bandpass filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=harmonics" title=" harmonics"> harmonics</a>, <a href="https://publications.waset.org/abstracts/search?q=envelope%20spectrum%20analysis" title=" envelope spectrum analysis"> envelope spectrum analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20kurtosis" title=" spectral kurtosis"> spectral kurtosis</a> </p> <a href="https://publications.waset.org/abstracts/169008/application-of-envelope-spectrum-analysis-and-spectral-kurtosis-to-diagnose-debris-fault-in-bearing-using-acoustic-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">901</span> Automated Heart Sound Classification from Unsegmented Phonocardiogram Signals Using Time Frequency Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Masood%20Khan">Nadia Masood Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Salman%20Khan"> Muhammad Salman Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Gul%20Muhammad%20Khan"> Gul Muhammad Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title="pattern recognition">pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title="computer aided diagnosis">computer aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20sound%20classification" title=" heart sound classification"> heart sound classification</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20feature%20extraction" title=" and feature extraction"> and feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/95434/automated-heart-sound-classification-from-unsegmented-phonocardiogram-signals-using-time-frequency-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">263</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">900</span> Limbic Involvement in Visual Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deborah%20Zelinsky">Deborah Zelinsky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The retina filters millions of incoming signals into a smaller amount of exiting optic nerve fibers that travel to different portions of the brain. Most of the signals are for eyesight (called "image-forming" signals). However, there are other faster signals that travel "elsewhere" and are not directly involved with eyesight (called "non-image-forming" signals). This article centers on the neurons of the optic nerve connecting to parts of the limbic system. Eye care providers are currently looking at parvocellular and magnocellular processing pathways without realizing that those are part of an enormous "galaxy" of all the body systems. Lenses are modifying both non-image and image-forming pathways, taking A.M. Skeffington's seminal work one step further. Almost 100 years ago, he described the Where am I (orientation), Where is It (localization), and What is It (identification) pathways. Now, among others, there is a How am I (animation) and a Who am I (inclination, motivation, imagination) pathway. Classic eye testing considers pupils and often assesses posture and motion awareness, but classical prescriptions often overlook limbic involvement in visual processing. The limbic system is composed of the hippocampus, amygdala, hypothalamus, and anterior nuclei of the thalamus. The optic nerve's limbic connections arise from the intrinsically photosensitive retinal ganglion cells (ipRGC) through the "retinohypothalamic tract" (RHT). There are two main hypothalamic nuclei with direct photic inputs. These are the suprachiasmatic nucleus and the paraventricular nucleus. Other hypothalamic nuclei connected with retinal function, including mood regulation, appetite, and glucose regulation, are the supraoptic nucleus and the arcuate nucleus. The retino-hypothalamic tract is often overlooked when we prescribe eyeglasses. Each person is different, but the lenses we choose are influencing this fast processing, which affects each patient's aiming and focusing abilities. These signals arise from the ipRGC cells that were only discovered 20+ years ago and do not address the campana retinal interneurons that were only discovered 2 years ago. As eyecare providers, we are unknowingly altering such factors as lymph flow, glucose metabolism, appetite, and sleep cycles in our patients. It is important to know what we are prescribing as the visual processing evaluations expand past the 20/20 central eyesight. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neuromodulation" title="neuromodulation">neuromodulation</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20processing" title=" retinal processing"> retinal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=retinohypothalamic%20tract" title=" retinohypothalamic tract"> retinohypothalamic tract</a>, <a href="https://publications.waset.org/abstracts/search?q=limbic%20system" title=" limbic system"> limbic system</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20processing" title=" visual processing"> visual processing</a> </p> <a href="https://publications.waset.org/abstracts/174552/limbic-involvement-in-visual-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">85</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">899</span> Feature Extractions of EMG Signals during a Constant Workload Pedaling Exercise</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bing-Wen%20Chen">Bing-Wen Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Alvin%20W.%20Y.%20Su"> Alvin W. Y. Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Lin%20Wang"> Yu-Lin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electromyography (EMG) is one of the important indicators during exercise, as it is closely related to the level of muscle activations. This work quantifies the muscle conditions of the lower limbs in a constant workload exercise. Surface EMG signals of the vastus laterals (VL), vastus medialis (VM), rectus femoris (RF), gastrocnemius medianus (GM), gastrocnemius lateral (GL) and Soleus (SOL) were recorded from fourteen healthy males. The EMG signals were segmented in two phases: activation segment (AS) and relaxation segment (RS). Period entropy (PE), peak count (PC), zero crossing (ZC), wave length (WL), mean power frequency (MPF), median frequency (MDF) and root mean square (RMS) are calculated to provide the quantitative information of the measured EMG segments. The outcomes reveal that the PE, PC, ZC and RMS have significantly changed (<em>p</em><.001); WL presents moderately changed (<em>p</em><.01); MPF and MDF show no changed (<em>p</em>>.05) during exercise. The results also suggest that the RS is also preferred for performance evaluation, while the results of the extracted features in AS are usually affected directly by the amplitudes. It is further found that the VL exhibits the most significant changes within six muscles during pedaling exercise. The proposed work could be applied to quantify the stamina analysis and to predict the instant muscle status in athletes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromyographic%20feature%20extraction" title="electromyographic feature extraction">electromyographic feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=muscle%20status" title=" muscle status"> muscle status</a>, <a href="https://publications.waset.org/abstracts/search?q=pedaling%20exercise" title=" pedaling exercise"> pedaling exercise</a>, <a href="https://publications.waset.org/abstracts/search?q=relaxation%20segment" title=" relaxation segment"> relaxation segment</a> </p> <a href="https://publications.waset.org/abstracts/49255/feature-extractions-of-emg-signals-during-a-constant-workload-pedaling-exercise" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">303</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">898</span> 3D Interferometric Imaging Using Compressive Hardware Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mor%20Diama%20L.%20O.">Mor Diama L. O.</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthieu%20Davy"> Matthieu Davy</a>, <a href="https://publications.waset.org/abstracts/search?q=Laurent%20Ferro-Famil"> Laurent Ferro-Famil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, inverse synthetic aperture radar (ISAR) is combined with compressive imaging techniques in order to perform 3D interferometric imaging. Interferometric ISAR (InISAR) imaging relies on a two-dimensional antenna array providing diversities in the elevation and azimuth directions. However, the signals measured over several antennas must be acquired by coherent receivers resulting in costly and complex hardware. This paper proposes to use a chaotic cavity as a compressive device to encode the signals arising from several antennas into a single output port. These signals are then reconstructed by solving an inverse problem. Our approach is demonstrated experimentally with a 3-elements L-shape array connected to a metallic compressive enclosure. The interferometric phases estimated from a unique broadband signal are used to jointly estimate the target’s effective rotation rate and the height of the dominant scattering centers of our target. Our experimental results show that the use of the compressive device does not adversely affect the performance of our imaging process. This study opens new perspectives to reduce the hardware complexity of high-resolution ISAR systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interferometric%20imaging" title="interferometric imaging">interferometric imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20synthetic%20aperture%20radar" title=" inverse synthetic aperture radar"> inverse synthetic aperture radar</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20device" title=" compressive device"> compressive device</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20imaging" title=" computational imaging"> computational imaging</a> </p> <a href="https://publications.waset.org/abstracts/134472/3d-interferometric-imaging-using-compressive-hardware-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134472.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">897</span> The Classification of Parkinson Tremor and Essential Tremor Based on Frequency Alteration of Different Activities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chusak%20Thanawattano">Chusak Thanawattano</a>, <a href="https://publications.waset.org/abstracts/search?q=Roongroj%20Bhidayasiri"> Roongroj Bhidayasiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a novel feature set utilized for classifying the Parkinson tremor and essential tremor. Ten ET and ten PD subjects are asked to perform kinetic, postural and resting tests. The empirical mode decomposition (EMD) is used to decompose collected tremor signal to a set of intrinsic mode functions (IMF). The IMFs are used for reconstructing representative signals. The feature set is composed of peak frequencies of IMFs and reconstructed signals. Hypothesize that the dominant frequency components of subjects with PD and ET change in different directions for different tests, difference of peak frequencies of IMFs and reconstructed signals of pairwise based tests (kinetic-resting, kinetic-postural and postural-resting) are considered as potential features. Sets of features are used to train and test by classifier including the quadratic discriminant classifier (QLC) and the support vector machine (SVM). The best accuracy, the best sensitivity and the best specificity are 90%, 87.5%, and 92.86%, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tremor" title="tremor">tremor</a>, <a href="https://publications.waset.org/abstracts/search?q=Parkinson" title=" Parkinson"> Parkinson</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20tremor" title=" essential tremor"> essential tremor</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20discriminant" title=" quadratic discriminant"> quadratic discriminant</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20frequency" title=" peak frequency"> peak frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-regressive" title=" auto-regressive"> auto-regressive</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum%20estimation" title=" spectrum estimation "> spectrum estimation </a> </p> <a href="https://publications.waset.org/abstracts/12449/the-classification-of-parkinson-tremor-and-essential-tremor-based-on-frequency-alteration-of-different-activities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12449.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">896</span> Implementation of a Monostatic Microwave Imaging System using a UWB Vivaldi Antenna</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babatunde%20Olatujoye">Babatunde Olatujoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Binbin%20Yang"> Binbin Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microwave imaging is a portable, noninvasive, and non-ionizing imaging technique that employs low-power microwave signals to reveal objects in the microwave frequency range. This technique has immense potential for adoption in commercial and scientific applications such as security scanning, material characterization, and nondestructive testing. This work presents a monostatic microwave imaging setup using an Ultra-Wideband (UWB), low-cost, miniaturized Vivaldi antenna with a bandwidth of 1 – 6 GHz. The backscattered signals (S-parameters) of the Vivaldi antenna used for scanning targets were measured in the lab using a VNA. An automated two-dimensional (2-D) scanner was employed for the 2-D movement of the transceiver to collect the measured scattering data from different positions. The targets consist of four metallic objects, each with a distinct shape. Similar setup was also simulated in Ansys HFSS. A high-resolution Back Propagation Algorithm (BPA) was applied to both the simulated and experimental backscattered signals. The BPA utilizes the phase and amplitude information recorded over a two-dimensional aperture of 50 cm × 50 cm with a discreet step size of 2 cm to reconstruct a focused image of the targets. The adoption of BPA was demonstrated by coherently resolving and reconstructing reflection signals from conventional time-of-flight profiles. For both the simulation and experimental data, BPA accurately reconstructed a high resolution 2D image of the targets in terms of shape and location. An improvement of the BPA, in terms of target resolution, was achieved by applying the filtering method in frequency domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back%20propagation" title="back propagation">back propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=microwave%20imaging" title=" microwave imaging"> microwave imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=monostatic" title=" monostatic"> monostatic</a>, <a href="https://publications.waset.org/abstracts/search?q=vivialdi%20antenna" title=" vivialdi antenna"> vivialdi antenna</a>, <a href="https://publications.waset.org/abstracts/search?q=ultra%20wideband" title=" ultra wideband"> ultra wideband</a> </p> <a href="https://publications.waset.org/abstracts/192577/implementation-of-a-monostatic-microwave-imaging-system-using-a-uwb-vivaldi-antenna" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192577.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">19</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">895</span> Application of the Bionic Wavelet Transform and Psycho-Acoustic Model for Speech Compression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chafik%20Barnoussi">Chafik Barnoussi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Talbi"> Mourad Talbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnane%20Cherif"> Adnane Cherif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose a new speech compression system based on the application of the Bionic Wavelet Transform (BWT) combined with the psychoacoustic model. This compression system is a modified version of the compression system using a MDCT (Modified Discrete Cosine Transform) filter banks of 32 filters each and the psychoacoustic model. This modification consists in replacing the banks of the MDCT filter banks by the bionic wavelet coefficients which are obtained from the application of the BWT to the speech signal to be compressed. These two methods are evaluated and compared with each other by computing bits before and bits after compression. They are tested on different speech signals and the obtained simulation results show that the proposed technique outperforms the second technique and this in term of compressed file size. In term of SNR, PSNR and NRMSE, the outputs speech signals of the proposed compression system are with acceptable quality. In term of PESQ and speech signal intelligibility, the proposed speech compression technique permits to obtain reconstructed speech signals with good quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20compression" title="speech compression">speech compression</a>, <a href="https://publications.waset.org/abstracts/search?q=bionic%20wavelet%20transform" title=" bionic wavelet transform"> bionic wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=filterbanks" title=" filterbanks"> filterbanks</a>, <a href="https://publications.waset.org/abstracts/search?q=psychoacoustic%20model" title=" psychoacoustic model"> psychoacoustic model</a> </p> <a href="https://publications.waset.org/abstracts/1921/application-of-the-bionic-wavelet-transform-and-psycho-acoustic-model-for-speech-compression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1921.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">384</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">894</span> Textile-Based Sensing System for Sleep Apnea Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mary%20S.%20Ruppert-Stroescu">Mary S. Ruppert-Stroescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Minh%20Pham"> Minh Pham</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruce%20Benjamin"> Bruce Benjamin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sleep apnea is a condition where a person stops breathing and can lead to cardiovascular disease, hypertension, and stroke. In the United States, approximately forty percent of overnight sleep apnea detection tests are cancelled. The purpose of this study was to develop a textile-based sensing system that acquires biometric signals relevant to cardiovascular health, to transmit them wirelessly to a computer, and to quantitatively assess the signals for sleep apnea detection. Patient interviews, literature review and market analysis defined a need for a device that ubiquitously integrated into the patient’s lifestyle. A multi-disciplinary research team of biomedical scientists, apparel designers, and computer engineers collaborated to design a textile-based sensing system that gathers EKG, Sp02, and respiration, then wirelessly transmits the signals to a computer in real time. The electronic components were assembled from existing hardware, the Health Kit which came pre-set with EKG and Sp02 sensors. The respiration belt was purchased separately and its electronics were built and integrated into the Health Kit mother board. Analog ECG signals were amplified and transmitted to the Arduino™ board where the signal was converted from analog into digital. By using textile electrodes, ECG lead-II was collected, and it reflected the electrical activity of the heart. Signals were collected when the subject was in sitting position and at sampling rate of 250 Hz. Because sleep apnea most often occurs in people with obese body types, prototypes were developed for a man’s size medium, XL, and XXL. To test user acceptance and comfort, wear tests were performed on 12 subjects. Results of the wear tests indicate that the knit fabric and t-shirt-like design were acceptable from both lifestyle and comfort perspectives. The airflow signal and respiration signal sensors return good signals regardless of movement intensity. Future study includes reconfiguring the hardware to a smaller size, developing the same type of garment for the female body, and further enhancing the signal quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sleep%20apnea" title="sleep apnea">sleep apnea</a>, <a href="https://publications.waset.org/abstracts/search?q=sensors" title=" sensors"> sensors</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20textiles" title=" electronic textiles"> electronic textiles</a>, <a href="https://publications.waset.org/abstracts/search?q=wearables" title=" wearables"> wearables</a> </p> <a href="https://publications.waset.org/abstracts/83334/textile-based-sensing-system-for-sleep-apnea-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83334.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">893</span> Improved Multi-Channel Separation Algorithm for Satellite-Based Automatic Identification System Signals Based on Artificial Bee Colony and Adaptive Moment Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Li">Peng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Luan%20Wang"> Luan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haifeng%20Fei"> Haifeng Fei</a>, <a href="https://publications.waset.org/abstracts/search?q=Renhong%20Xie"> Renhong Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Yibin%20Rui"> Yibin Rui</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanhong%20Guo"> Shanhong Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite-based%20automatic%20identification%20system" title="satellite-based automatic identification system">satellite-based automatic identification system</a>, <a href="https://publications.waset.org/abstracts/search?q=blind%20source%20separation" title=" blind source separation"> blind source separation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20moment%20estimation" title=" adaptive moment estimation"> adaptive moment estimation</a> </p> <a href="https://publications.waset.org/abstracts/86895/improved-multi-channel-separation-algorithm-for-satellite-based-automatic-identification-system-signals-based-on-artificial-bee-colony-and-adaptive-moment-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86895.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">892</span> Transient Analysis of Central Region Void Fraction in a 3x3 Rod Bundle under Bubbly and Cap/Slug Flows</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ya-Chi%20Yu">Ya-Chi Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Pei-Syuan%20Ruan"> Pei-Syuan Ruan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shao-Wen%20Chen"> Shao-Wen Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Hsien%20Chang"> Yu-Hsien Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin-Der%20Lee"> Jin-Der Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong-Rong%20Wang"> Jong-Rong Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chunkuan%20Shih"> Chunkuan Shih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study analyzed the transient signals of central region void fraction of air-water two-phase flow in a 3x3 rod bundle. Experimental tests were carried out utilizing a vertical rod bundle test section along with a set of air-water supply/flow control system, and the transient signals of the central region void fraction were collected through the electrical conductivity sensors as well as visualized via high speed photography. By converting the electric signals, transient void fraction can be obtained through the voltage ratios. With a fixed superficial water velocity (J<sub>f</sub>=0.094 m/s), two different superficial air velocities (J<sub>g</sub>=0.094 m/s and 0.236 m/s) were tested and presented, which were corresponding to the flow conditions of bubbly flows and cap/slug flows, respectively. The time averaged central region void fraction was obtained as 0.109-0.122 with 0.028 standard deviation for the selected bubbly flow and 0.188-0.221with 0.101 standard deviation for the selected cap/slug flow, respectively. Through Fast Fourier Transform (FFT) analysis, no clear frequency peak was found in bubbly flow, while two dominant frequencies were identified around 1.6 Hz and 2.5 Hz in the present cap/slug flow. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20region" title="central region">central region</a>, <a href="https://publications.waset.org/abstracts/search?q=rod%20bundles" title=" rod bundles"> rod bundles</a>, <a href="https://publications.waset.org/abstracts/search?q=transient%20void%20fraction" title=" transient void fraction"> transient void fraction</a>, <a href="https://publications.waset.org/abstracts/search?q=two-phase%20flow" title=" two-phase flow"> two-phase flow</a> </p> <a href="https://publications.waset.org/abstracts/99136/transient-analysis-of-central-region-void-fraction-in-a-3x3-rod-bundle-under-bubbly-and-capslug-flows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99136.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">185</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">891</span> Identification of EEG Attention Level Using Empirical Mode Decompositions for BCI Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Ju%20Peng">Chia-Ju Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shih-Jui%20Chen"> Shih-Jui Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a method to discriminate electroencephalogram (EEG) signals between different concentration states using empirical mode decomposition (EMD). Brain-computer interface (BCI), also called brain-machine interface, is a direct communication pathway between the brain and an external device without the inherent pathway such as the peripheral nervous system or skeletal muscles. Attention level is a common index as a control signal of BCI systems. The EEG signals acquired from people paying attention or in relaxation, respectively, are decomposed into a set of intrinsic mode functions (IMF) by EMD. Fast Fourier transform (FFT) analysis is then applied to each IMF to obtain the frequency spectrums. By observing power spectrums of IMFs, the proposed method has the better identification of EEG attention level than the original EEG signals between different concentration states. The band power of IMF3 is the most obvious especially in β wave, which corresponds to fully awake and generally alert. The signal processing method and results of this experiment paves a new way for BCI robotic system using the attention-level control strategy. The integrated signal processing method reveals appropriate information for discrimination of the attention and relaxation, contributing to a more enhanced BCI performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering" title="biomedical engineering">biomedical engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20computer%20interface" title=" brain computer interface"> brain computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalography" title=" electroencephalography"> electroencephalography</a>, <a href="https://publications.waset.org/abstracts/search?q=rehabilitation" title=" rehabilitation"> rehabilitation</a> </p> <a href="https://publications.waset.org/abstracts/31042/identification-of-eeg-attention-level-using-empirical-mode-decompositions-for-bci-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31042.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">391</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">890</span> Non-Uniform Filter Banks-based Minimum Distance to Riemannian Mean Classifition in Motor Imagery Brain-Computer Interface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ping%20Tan">Ping Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaomeng%20Su"> Xiaomeng Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi%20Shen"> Yi Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-uniform%20filter%20banks" title="non-uniform filter banks">non-uniform filter banks</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20imagery" title=" motor imagery"> motor imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title=" brain-computer interface"> brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20distance%20to%20Riemannian%20mean" title=" minimum distance to Riemannian mean"> minimum distance to Riemannian mean</a> </p> <a href="https://publications.waset.org/abstracts/162018/non-uniform-filter-banks-based-minimum-distance-to-riemannian-mean-classifition-in-motor-imagery-brain-computer-interface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162018.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">889</span> Methods for Enhancing Ensemble Learning or Improving Classifiers of This Technique in the Analysis and Classification of Brain Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mehdi%20Ghezi">Seyed Mehdi Ghezi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hesam%20Hasanpoor"> Hesam Hasanpoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ensemble%20learning" title="ensemble learning">ensemble learning</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20signals" title=" brain signals"> brain signals</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20methods" title=" optimization methods"> optimization methods</a>, <a href="https://publications.waset.org/abstracts/search?q=influential%20features" title=" influential features"> influential features</a>, <a href="https://publications.waset.org/abstracts/search?q=influential%20electrodes" title=" influential electrodes"> influential electrodes</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-classifiers" title=" meta-classifiers"> meta-classifiers</a> </p> <a href="https://publications.waset.org/abstracts/177312/methods-for-enhancing-ensemble-learning-or-improving-classifiers-of-this-technique-in-the-analysis-and-classification-of-brain-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177312.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">888</span> Analysis of Epileptic Electroencephalogram Using Detrended Fluctuation and Recurrence Plots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinalini%20Ranjan">Mrinalini Ranjan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudheesh%20Chethil"> Sudheesh Chethil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a common neurological disorder characterised by the recurrence of seizures. Electroencephalogram (EEG) signals are complex biomedical signals which exhibit nonlinear and nonstationary behavior. We use two methods 1) Detrended Fluctuation Analysis (DFA) and 2) Recurrence Plots (RP) to capture this complex behavior of EEG signals. DFA considers fluctuation from local linear trends. Scale invariance of these signals is well captured in the multifractal characterisation using detrended fluctuation analysis (DFA). Analysis of long-range correlations is vital for understanding the dynamics of EEG signals. Correlation properties in the EEG signal are quantified by the calculation of a scaling exponent. We report the existence of two scaling behaviours in the epileptic EEG signals which quantify short and long-range correlations. To illustrate this, we perform DFA on extant ictal (seizure) and interictal (seizure free) datasets of different patients in different channels. We compute the short term and long scaling exponents and report a decrease in short range scaling exponent during seizure as compared to pre-seizure and a subsequent increase during post-seizure period, while the long-term scaling exponent shows an increase during seizure activity. Our calculation of long-term scaling exponent yields a value between 0.5 and 1, thus pointing to power law behaviour of long-range temporal correlations (LRTC). We perform this analysis for multiple channels and report similar behaviour. We find an increase in the long-term scaling exponent during seizure in all channels, which we attribute to an increase in persistent LRTC during seizure. The magnitude of the scaling exponent and its distribution in different channels can help in better identification of areas in brain most affected during seizure activity. The nature of epileptic seizures varies from patient-to-patient. To illustrate this, we report an increase in long-term scaling exponent for some patients which is also complemented by the recurrence plots (RP). RP is a graph that shows the time index of recurrence of a dynamical state. We perform Recurrence Quantitative analysis (RQA) and calculate RQA parameters like diagonal length, entropy, recurrence, determinism, etc. for ictal and interictal datasets. We find that the RQA parameters increase during seizure activity, indicating a transition. We observe that RQA parameters are higher during seizure period as compared to post seizure values, whereas for some patients post seizure values exceeded those during seizure. We attribute this to varying nature of seizure in different patients indicating a different route or mechanism during the transition. Our results can help in better understanding of the characterisation of epileptic EEG signals from a nonlinear analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detrended%20fluctuation" title="detrended fluctuation">detrended fluctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20range%20correlations" title=" long range correlations"> long range correlations</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrence%20plots" title=" recurrence plots"> recurrence plots</a> </p> <a href="https://publications.waset.org/abstracts/84822/analysis-of-epileptic-electroencephalogram-using-detrended-fluctuation-and-recurrence-plots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">887</span> Robot Navigation and Localization Based on the Rat’s Brain Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endri%20Rama">Endri Rama</a>, <a href="https://publications.waset.org/abstracts/search?q=Genci%20Capi"> Genci Capi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shigenori%20Kawahara"> Shigenori Kawahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-machine%20interface" title="brain-machine interface">brain-machine interface</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-making" title=" decision-making"> decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20robot" title=" mobile robot"> mobile robot</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/68202/robot-navigation-and-localization-based-on-the-rats-brain-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68202.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">297</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">886</span> Non-Parametric Changepoint Approximation for Road Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lo%C3%AFc%20Warscotte">Loïc Warscotte</a>, <a href="https://publications.waset.org/abstracts/search?q=Jehan%20Boreux"> Jehan Boreux</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The scientific literature of changepoint detection is vast. Today, a lot of methods are available to detect abrupt changes or slight drift in a signal, based on CUSUM or EWMA charts, for example. However, these methods rely on strong assumptions, such as the stationarity of the stochastic underlying process, or even the independence and Gaussian distributed noise at each time. Recently, the breakthrough research on locally stationary processes widens the class of studied stochastic processes with almost no assumptions on the signals and the nature of the changepoint. Despite the accurate description of the mathematical aspects, this methodology quickly suffers from impractical time and space complexity concerning the signals with high-rate data collection, if the characteristics of the process are completely unknown. In this paper, we then addressed the problem of making this theory usable to our purpose, which is monitoring a high-speed weigh-in-motion system (HS-WIM) towards direct enforcement without supervision. To this end, we first compute bounded approximations of the initial detection theory. Secondly, these approximating bounds are empirically validated by generating many independent long-run stochastic processes. The abrupt changes and the drift are both tested. Finally, this relaxed methodology is tested on real signals coming from a HS-WIM device in Belgium, collected over several months. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=changepoint" title="changepoint">changepoint</a>, <a href="https://publications.waset.org/abstracts/search?q=weigh-in-motion" title=" weigh-in-motion"> weigh-in-motion</a>, <a href="https://publications.waset.org/abstracts/search?q=process" title=" process"> process</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric" title=" non-parametric"> non-parametric</a> </p> <a href="https://publications.waset.org/abstracts/181037/non-parametric-changepoint-approximation-for-road-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181037.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">885</span> Detection and Classification of Myocardial Infarction Using New Extracted Features from Standard 12-Lead ECG Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naser%20Safdarian">Naser Safdarian</a>, <a href="https://publications.waset.org/abstracts/search?q=Nader%20Jafarnia%20Dabanloo"> Nader Jafarnia Dabanloo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECG%20signal%20processing" title="ECG signal processing">ECG signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=myocardial%20infarction" title=" myocardial infarction"> myocardial infarction</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/38349/detection-and-classification-of-myocardial-infarction-using-new-extracted-features-from-standard-12-lead-ecg-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38349.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">456</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">884</span> Entropy Risk Factor Model of Exchange Rate Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darrol%20Stanley">Darrol Stanley</a>, <a href="https://publications.waset.org/abstracts/search?q=Levan%20Efremidze"> Levan Efremidze</a>, <a href="https://publications.waset.org/abstracts/search?q=Jannie%20Rossouw"> Jannie Rossouw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We investigate the predictability of the USD/ZAR (South African Rand) exchange rate with sample entropy analytics for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. These results are preliminary and do not yet account for reasonable transactions costs, although these are very small in currency markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=currency%20trading" title="currency trading">currency trading</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20timing" title=" market timing"> market timing</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20factor%20model" title=" risk factor model"> risk factor model</a> </p> <a href="https://publications.waset.org/abstracts/53853/entropy-risk-factor-model-of-exchange-rate-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">271</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">883</span> Electroencephalogram Based Approach for Mental Stress Detection during Gameplay with Level Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priyadarsini%20Samal">Priyadarsini Samal</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Singla"> Rajesh Singla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20computer%20interface" title="brain computer interface">brain computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=stress" title=" stress"> stress</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20search" title=" word search"> word search</a> </p> <a href="https://publications.waset.org/abstracts/139736/electroencephalogram-based-approach-for-mental-stress-detection-during-gameplay-with-level-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139736.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">882</span> Multichannel Surface Electromyography Trajectories for Hand Movement Recognition Using Intrasubject and Intersubject Evaluations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christina%20Adly">Christina Adly</a>, <a href="https://publications.waset.org/abstracts/search?q=Meena%20Abdelmeseeh"> Meena Abdelmeseeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamer%20Basha"> Tamer Basha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hand%20movement%20recognition" title="hand movement recognition">hand movement recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=movement%20error%20rate" title=" movement error rate"> movement error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=intrasubject%20evaluation" title=" intrasubject evaluation"> intrasubject evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=intersubject%20evaluation" title=" intersubject evaluation"> intersubject evaluation</a> </p> <a href="https://publications.waset.org/abstracts/149564/multichannel-surface-electromyography-trajectories-for-hand-movement-recognition-using-intrasubject-and-intersubject-evaluations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">881</span> Theory of the Optimum Signal Approximation Clarifying the Importance in the Recognition of Parallel World and Application to Secure Signal Communication with Feedback</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takuro%20Kida">Takuro Kida</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuichi%20Kida"> Yuichi Kida</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, it is shown a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detail algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output-signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory, and it is indicated that introducing conversations with feedback do not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=matrix%20filterbank" title="matrix filterbank">matrix filterbank</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20signal%20approximation" title=" optimum signal approximation"> optimum signal approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20theory" title=" category theory"> category theory</a>, <a href="https://publications.waset.org/abstracts/search?q=simultaneous%20minimization" title=" simultaneous minimization"> simultaneous minimization</a> </p> <a href="https://publications.waset.org/abstracts/150610/theory-of-the-optimum-signal-approximation-clarifying-the-importance-in-the-recognition-of-parallel-world-and-application-to-secure-signal-communication-with-feedback" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150610.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">880</span> Classification of Myoelectric Signals Using Multilayer Perceptron Neural Network with Back-Propagation Algorithm in a Wireless Surface Myoelectric Prosthesis of the Upper-Limb</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20D.%20Manalo">Kevin D. Manalo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jumelyn%20L.%20Torres"> Jumelyn L. Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Noel%20B.%20Linsangan"> Noel B. Linsangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title="field programmable gate array">field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20network" title=" multilayer perceptron neural network"> multilayer perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=verilog" title=" verilog"> verilog</a>, <a href="https://publications.waset.org/abstracts/search?q=zigbee" title=" zigbee"> zigbee</a> </p> <a href="https://publications.waset.org/abstracts/19846/classification-of-myoelectric-signals-using-multilayer-perceptron-neural-network-with-back-propagation-algorithm-in-a-wireless-surface-myoelectric-prosthesis-of-the-upper-limb" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19846.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">879</span> The Use of Network Tool for Brain Signal Data Analysis: A Case Study with Blind and Sighted Individuals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cleiton%20Pons%20Ferreira">Cleiton Pons Ferreira</a>, <a href="https://publications.waset.org/abstracts/search?q=Diana%20Francisca%20Adamatti"> Diana Francisca Adamatti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advancements in computers technology have allowed to obtain information for research in biology and neuroscience. In order to transform the data from these surveys, networks have long been used to represent important biological processes, changing the use of this tools from purely illustrative and didactic to more analytic, even including interaction analysis and hypothesis formulation. Many studies have involved this application, but not directly for interpretation of data obtained from brain functions, asking for new perspectives of development in neuroinformatics using existent models of tools already disseminated by the bioinformatics. This study includes an analysis of neurological data through electroencephalogram (EEG) signals, using the Cytoscape, an open source software tool for visualizing complex networks in biological databases. The data were obtained from a comparative case study developed in a research from the University of Rio Grande (FURG), using the EEG signals from a Brain Computer Interface (BCI) with 32 eletrodes prepared in the brain of a blind and a sighted individuals during the execution of an activity that stimulated the spatial ability. This study intends to present results that lead to better ways for use and adapt techniques that support the data treatment of brain signals for elevate the understanding and learning in neuroscience. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neuroinformatics" title="neuroinformatics">neuroinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20tools" title=" network tools"> network tools</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20mapping" title=" brain mapping"> brain mapping</a> </p> <a href="https://publications.waset.org/abstracts/105037/the-use-of-network-tool-for-brain-signal-data-analysis-a-case-study-with-blind-and-sighted-individuals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105037.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">878</span> Analysis of Real Time Seismic Signal Dataset Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujata%20Kulkarni">Sujata Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Udhav%20Bhosle"> Udhav Bhosle</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijaykumar%20T."> Vijaykumar T.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carl%20STA%2FLTA" title="Carl STA/LTA">Carl STA/LTA</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time" title=" real time"> real time</a>, <a href="https://publications.waset.org/abstracts/search?q=dataset" title=" dataset"> dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20detection" title=" seismic detection"> seismic detection</a> </p> <a href="https://publications.waset.org/abstracts/167028/analysis-of-real-time-seismic-signal-dataset-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">877</span> Bundle Block Detection Using Spectral Coherence and Levenberg Marquardt Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Padmavathi">K. Padmavathi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sri%20Ramakrishna"> K. Sri Ramakrishna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bundle%20block" title="bundle block">bundle block</a>, <a href="https://publications.waset.org/abstracts/search?q=SC" title=" SC"> SC</a>, <a href="https://publications.waset.org/abstracts/search?q=LMNN%20classifier" title=" LMNN classifier"> LMNN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=welch%20method" title=" welch method"> welch method</a>, <a href="https://publications.waset.org/abstracts/search?q=PSD" title=" PSD"> PSD</a>, <a href="https://publications.waset.org/abstracts/search?q=MIT-BIH" title=" MIT-BIH"> MIT-BIH</a>, <a href="https://publications.waset.org/abstracts/search?q=arrhythmia%20database" title=" arrhythmia database"> arrhythmia database</a> </p> <a href="https://publications.waset.org/abstracts/17530/bundle-block-detection-using-spectral-coherence-and-levenberg-marquardt-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">876</span> Recognizing an Individual, Their Topic of Conversation and Cultural Background from 3D Body Movement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gheida%20J.%20Shahrour">Gheida J. Shahrour</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20J.%20Russell"> Martin J. Russell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=person%20recognition" title="person recognition">person recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20recognition" title=" topic recognition"> topic recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=culture%20recognition" title=" culture recognition"> culture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20body%20movement%20signals" title=" 3D body movement signals"> 3D body movement signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variability%20compensation" title=" variability compensation"> variability compensation</a> </p> <a href="https://publications.waset.org/abstracts/19473/recognizing-an-individual-their-topic-of-conversation-and-cultural-background-from-3d-body-movement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">541</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">875</span> Features Dimensionality Reduction and Multi-Dimensional Voice-Processing Program to Parkinson Disease Discrimination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Djamila%20Meghraoui">Djamila Meghraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Bachir%20Boudraa"> Bachir Boudraa</a>, <a href="https://publications.waset.org/abstracts/search?q=Thouraya%20Meksen"> Thouraya Meksen</a>, <a href="https://publications.waset.org/abstracts/search?q=M.Boudraa"> M.Boudraa </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parkinson%E2%80%99s%20disease%20recognition" title="Parkinson’s disease recognition">Parkinson’s disease recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=MDVP" title=" MDVP"> MDVP</a>, <a href="https://publications.waset.org/abstracts/search?q=multinomial%20Naive%20Bayes" title=" multinomial Naive Bayes"> multinomial Naive Bayes</a> </p> <a href="https://publications.waset.org/abstracts/59181/features-dimensionality-reduction-and-multi-dimensional-voice-processing-program-to-parkinson-disease-discrimination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59181.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">874</span> Filtering Momentum Life Cycles, Price Acceleration Signals and Trend Reversals for Stocks, Credit Derivatives and Bonds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Periklis%20Brakatsoulas">Periklis Brakatsoulas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20memory" title=" long memory"> long memory</a>, <a href="https://publications.waset.org/abstracts/search?q=momentum" title=" momentum"> momentum</a>, <a href="https://publications.waset.org/abstracts/search?q=returns" title=" returns"> returns</a> </p> <a href="https://publications.waset.org/abstracts/106031/filtering-momentum-life-cycles-price-acceleration-signals-and-trend-reversals-for-stocks-credit-derivatives-and-bonds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">873</span> Correlation Analysis between Physical Fitness Norm and Cardio-Pulmonary Signals under Graded Exercise and Recovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shyan-Lung%20Lin">Shyan-Lung Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Yi%20Huang"> Cheng-Yi Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Tung-Yi%20Lin"> Tung-Yi Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Physical fitness is the adaptability of the body to physical work and the environment, and is generally known to include cardiopulmonary-fitness, muscular-fitness, body flexibility, and body composition. This paper is aimed to study the ventilatory and cardiovascular activity under various exercise intensities for subjects at distinct ends of cardiopulmonary fitness norm. Three graded upright biking exercises, light, moderate, and vigorous exercise, were designed for subjects at distinct ends of cardiopulmonary fitness norm from their physical education classes. The participants in the experiments were 9, 9, and 11 subjects in the top 20%, middle 20%, and bottom 20%, respectively, among all freshmen of the Feng Chia University in the academic year of 2015. All participants were requested to perform 5 minutes of upright biking exercise to attain 50%, 65%, and 85% of their maximum heart rate (HRmax) during the light, moderate, and vigorous exercise experiment, respectively, and 5 minutes of recovery following each graded exercise. The cardiovascular and ventilatory signals, including breathing frequency (f), tidal volume (VT), heart rate (HR), mean arterial pressure (MAP), and ECG signals were recorded during rest, exercise, and recovery periods. The physiological signals of three groups were analyzed based on their recovery, recovery rate, and percentage variation from rest. Selected time domain parameters, SDNN and RMSSD, were computed and spectral analysis was performed to study the hear rate variability from collected ECG signals. The comparison studies were performed to examine the correlations between physical fitness norm and cardio-pulmonary signals during graded exercises and exercise recovery. No significant difference was found among three groups with VT during all levels of exercise intensity and recovery. The top 20% group was found to have better performance in heart recovery (HRR), frequency recovery rate (fRR) and percentage variation from rest (Δf) during the recovery period of vigorous exercise. The top 20% group was also found to achieve lower mean arterial pressure MAP only at rest but showed no significant difference during graded exercises and recovery periods. In time-domain analysis of HRV, the top 20% group again seemed to have better recovery rate and less variation in terms of SDNN during recovery period of light and vigorous exercises. Most assessed frequency domain parameters changed significantly during the experiment (p<0.05, ANOVA). The analysis showed that the top 20% group, in comparison with middle and bottom 20% groups, appeared to have significantly higher TP, LF, HF, and nHF index, while the bottom 20% group showed higher nLF and LF/HF index during rest, three graded levels of exercises, and their recovery periods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physical%20fitness" title="physical fitness">physical fitness</a>, <a href="https://publications.waset.org/abstracts/search?q=cardio-pulmonary%20signals" title=" cardio-pulmonary signals"> cardio-pulmonary signals</a>, <a href="https://publications.waset.org/abstracts/search?q=graded%20exercise" title=" graded exercise"> graded exercise</a>, <a href="https://publications.waset.org/abstracts/search?q=exercise%20recovery" title=" exercise recovery"> exercise recovery</a> </p> <a href="https://publications.waset.org/abstracts/58526/correlation-analysis-between-physical-fitness-norm-and-cardio-pulmonary-signals-under-graded-exercise-and-recovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">258</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signals&page=3" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signals&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=signals&page=2">2</a></li> <li class="page-item"><a class="page-link" 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